Data Scientist, Reliability Analytics

Key Skills

Java Programming, Python Programming, SQL, R Programming, Logging, Hypothesis testing

Job Description

Minimum qualifications:

• Master's degree in Statistics, Computer Science, Mathematics, Operations Research, or equivalent practical experience.
• Experience with statistical software and programming (R, Python) and database languages (SQL).
• Experience in designing experiments (e.g., logging, hypothesis testing, treatment effects, causal inference).
• Experience with data analysis statistical methods such as modeling, testing, and inference.

Preferred qualifications:

• PhD in Statistics, Computer Science, Applied Mathematics, or Operations Research, or related fields.
• Experience in working with large-scale data systems for acquisition, storage, and management of complex data flow.
• Experience in deploying and analyzing experiments (e.g., design of experiments, experiment logging, multiple hypothesis testing, heterogeneous treatment effects, causal inference).
• Experience in predictive modeling such as machine learning and time series models.
• Experience with the standard machine learning techniques and popular ML libraries.
• Knowledge of statistical reliability, engineering risk analysis, queueing theory, and analysis of time processes.

About the job

At Google, data drives all of our decision-making. Quantitative Analysts work all across the organization to help shape Google's business and technical strategies by processing, analyzing and interpreting huge data sets. Using analytical excellence and statistical methods, you mine through data to identify opportunities for Google and our clients to operate more efficiently, from enhancing advertising efficacy to network infrastructure optimization to studying user behavior. As an analyst, you do more than just crunch the numbers. You work with Engineers, Product Managers, Sales Associates and Marketing teams to adjust Google's practices according to your findings. Identifying the problem is only half the job; you also figure out the solution.

Reliability Analytics is a group of data scientists and product analysts tasked with developing a comprehensive quantitative understanding of all aspects of reliability for Google infrastructure, systems, and products. This team has the mission of improving the robustness, resiliency, and recovery capabilities and dynamics of all Google infrastructure, and develop these solutions using advanced data analytics, statistical machine learning, and high-fidelity mathematical modeling techniques. We apply data science and advanced quantitative techniques to solve data, modeling, and algorithmic problems for large-scale engineering systems.

As a Data Scientist working on the Reliability Analytics team, you will address complex investigations, solve profound problems for large-scale integrated technology systems, and make an impact. You will focus on designing and prototyping analytical/simulation frameworks and pipelines, designing and analyzing evaluation experiments, extracting signals and drawing insights from datasets. Your work will proceed across all technology resource inventories and under realistic operational and deployment policies.

Responsibilities

• Design and prototype analytical/simulation frameworks and pipelines, and design and analyze evaluation experiments.
• Extract signals and draw insights from datasets across all technology resource inventories and under realistic operational and deployment policies. 
• Provide critical inputs to both short-term, tactical actions and long-term technology choices and strategies, and explore machine learning methods to better understand workload.
• Apply advanced quantitative techniques to solve complex data, quantitative modeling, and machine algorithmic problems in large-scale engineering systems.
• Explore machine learning methods to better characterize workload, statically as capacity and its dynamic behavior under operational stress.